Apparatus and method for estimating biometric information

ABSTRACT

An apparatus for estimating biometric information is provided. According to one exemplary embodiment, the apparatus may include a sensor comprising an electrocardiogram (ECG) sensor configured to measure an ECG signal of a user and a pulse wave sensor configured to measure two or more pulse wave signals at two or more measurement sites of the user; and a processor configured to obtain biometric information based on the ECG signal and the two or more pulse wave signals measured by the sensor.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation application of U.S. patentapplication Ser. No. 15/654,422, filed on Jul. 19, 2017, which claimspriority from Korean Patent Application No. 10-2016-0132228, filed onOct. 12, 2016 in the Korean Intellectual Property Office, the entiredisclosure of which is incorporated herein by reference in its entirety.

BACKGROUND 1. Field

Apparatuses and methods consistent with exemplary embodiments relate toestimating biometric information, and more specifically, estimatingbiometric information on the basis of biometric signals obtained atmultiple sites of a subject to be examined.

2. Description of Related Art

As a general blood pressure measurement method, a pressure cuff methodis used. This method is a non-continuous measurement method by which ablood pressure is measured by tightening a cuff around blood vessels toa point to reach the maximum blood pressure and loosening the cuff.Also, the method is not suitable for use in a wristwatch typemeasurement device due to the structure, such as a pressure pump.Recently, a non-pressure cuffless-type blood pressure measurement methodhas been studied. A general cuffless blood pressure measurement methoduses a correlation between a pulse transit time (PTT) and a bloodpressure, for example, a pulse wave or an electrocardiogram (ECG)measured at two different sites.

SUMMARY

According to an aspect of an exemplary embodiment, there is provided anapparatus for estimating biometric information including: a sensorincluding an electrocardiogram (ECG) sensor configured to measure an ECGsignal of a user and a pulse wave sensor configured to measure two ormore pulse wave signals at two or more measurement sites of the user;and a processor configured to receive the ECG signal and the two or morepulse wave signals from the sensor and obtain biometric informationbased on the received ECG and two or more pulse wave signals.

The apparatus may further include a main body and a strap connected tothe main body and formed to be flexible to wrap around at least one ofthe two or more measurement sites, wherein the sensor is mounted in themain body or the strap.

The two or more measurement sites of the user may include a firstmeasurement site and a second measurement site of the user. The ECGsensor may include a first electrode disposed at a first position of theapparatus to be in contact with the first measurement site of the userand a second electrode disposed at a second position of the apparatus tobe in contact with the second measurement site of the user.

The pulse wave sensor may include: a first pulse wave sensor configuredto emit light to the first measurement site, detect the light returningfrom the first measurement site, and obtain a first pulse wave signalfrom the light detected by the first pulse wave sensor; and a secondpulse wave sensor configured to emit light to the second measurementsite, detect the light returning from the second measurement site, andobtain a second pulse wave signal from the light detected by the secondpulse wave sensor.

The apparatus may further include a display configured to output thebiometric information according to a control signal of the processor.

The processor may include a transit time calculator configured todetermine at least three pulse transit times (PTTs) based on thereceived ECG and two or more pulse wave signals, and a first estimatorconfigured to apply the three or more PTTs to a first estimation modelto obtain first biometric information.

The processor may include a pulse wave analyzer configured to extract,from waveforms of the two or more pulse wave signals, reflected wavecharacteristic information indicating an impact of reflected waves ofthe two or more pulse wave signals on a change in waveform of the two ormore pulse wave signals, and a second estimator configured to apply theextracted reflected wave characteristic information to a secondestimation model to obtain second biometric information.

The pulse wave analyzer may be further configured to extract featurepoints from the two or more pulse wave signals, and extract thereflected wave characteristic information which comprises one or morefirst PTTs calculated using the feature points of different pulse wavesignals of the two or more pulse wave signals and one or more secondPTTs calculated using the feature points of a same pulse wave signal ofthe two or more pulse wave signals.

When a result is output by applying the extracted reflected wavecharacteristic information to the second estimation model, the secondestimator is further configured to obtain the second biometricinformation based on the output result and the first biometricinformation.

The first biometric information may be a diastolic blood pressure andthe second biometric information may be a systolic blood pressure.

The processor may be further configured to generate an estimation modelfor estimating the biometric information based on personal informationinput by the user, and the personal information may include one or moreof height, weight, sex, age, and a health condition of the user.

The processor may include a calibrator configured to obtain vascularresistance information based on waveforms of the two or more pulse wavesignals and calibrate the biometric information based on the vascularresistance information.

The apparatus may further include a communication interface configuredto receive reference biometric information from an external apparatus.The processor may include a calibrator configured to calibrate thebiometric information based on the reference biometric information.

The processor may be further configured to obtain the biometricinformation while the external apparatus obtains the reference biometricinformation of the user.

The calibrator is further configured to calibrate at least one of avalue of the biometric information, two or more pulse transit times(PTTs) calculated using the ECG signal and the two or more pulse wavesignals, and an estimation model for estimating the biometricinformation.

The external apparatus may include a cuff-type blood pressure estimatingapparatus and the reference biometric information includes at least oneof a cuff blood pressure estimated by the cuff-type blood pressureestimating apparatus and cuff pressure information.

According to an aspect of another exemplary embodiment, there isprovided a method of estimating biometric information including:measuring a user's electrocardiogram (ECG) signal; measuring two or morepulse wave signals from two or more measurement sites of the user; andobtaining biometric information based on the ECG signal and the two ormore pulse wave signals.

The obtaining the biometric information may include determining at leastthree pulse transit times (PTTs) based on the ECG signal and the two ormore pulse wave signals and applying the at least three PTTs to a firstestimation model to obtain first biometric information.

The obtaining the biometric information may include extracting, fromwaveforms of the two or more pulse wave signals, reflected wavecharacteristic information indicating an impact of reflected waves ofthe two or more pulse wave signals on a change in waveform of the two ormore pulse wave signals and applying the extracted reflected wavecharacteristic information to a second estimation model to obtain secondbiometric information.

The extracting the reflected wave characteristic information may includeextracting feature points from the two or more pulse wave signals andextracting the reflected wave characteristic information which includesone or more first PTTs calculated using the feature points of differentpulse wave signals of the two or more pulse wave signals and one or moresecond PTTs calculated using the feature points of a same pulse wavesignal of the two or more pulse wave signals.

When a result is output by applying the extracted reflected wavecharacteristic information to the second estimation model, the secondbiometric information may be obtained based on the output result and thefirst biometric information.

The method may further include: receiving personal information input bythe user, the personal information including one or more of height,weight, sex, age, and a health condition of the user; and generating anestimation model for estimating the biometric information based on thereceived personal information.

The method may further include: obtaining vascular resistanceinformation based on waveforms of the two or more pulse wave signals;and calibrating the biometric information based on the vascularresistance information to correct an error in the biometric information.

The method may further include: receiving reference biometricinformation from an external apparatus; and calibrating the biometricinformation based on the received reference biometric information.

The calibrating the biometric information may include calibrating atleast one of a value of the biometric information, two or more pulsetransit times (PTTs) calculated using the ECG signal and the two or morepulse wave signals, and an estimation model for estimating the biometricinformation.

The reference biometric information may include at least one of a cuffblood pressure estimated by a cuff-type blood pressure estimatingapparatus included in the external apparatus and cuff pressureinformation.

According to an aspect of another exemplary embodiment, there isprovided an apparatus for obtaining blood pressure informationincluding: a plurality of sensors configured to detect anelectrocardiogram (ECG) signal of a user and detect a plurality ofphotoplethysmography (PPG) signals at different measurement sites of theuser, the plurality of PPG signals including a first PPG signal and asecond PPG signal; and a processor configured to determine a firstdifferential pulse transit time (DPTT) between the ECG signal and thefirst PPG signal, a second DPTT between the ECG signal and the secondPPG signal, and a third DPTT between the first PPG signal and the secondPPG signal, and determine a blood pressure level of the user based onthe first DPTT, the second DPTT, and the third DPTT.

The processor may be further configured to receive information ofphysical characteristics of the user, set a maximum level and a minimumlevel for the blood pressure level to be determined, and determine theblood pressure level based on the first DPTT, the second DPTT, the thirdDPTT, and the physical characteristics of the user.

The physical characteristics may include one or more of height, weight,sex and age of the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects will be more apparent by describingcertain exemplary embodiments, with reference to the accompanyingdrawings, in which:

FIGS. 1A and 1B are diagrams illustrating the configuration of anapparatus for estimating biometric information according to an exemplaryembodiment of the present invention.

FIG. 2 is a block diagram illustrating an apparatus for estimatingbiometric information according to an exemplary embodiment.

FIG. 3 is a block diagram illustrating a sensor of the apparatus forestimating biometric information according to an exemplary embodiment.

FIG. 4 is a block diagram illustrating a processor of the apparatus forestimating biometric information according to an exemplary embodiment.

FIGS. 5A and 5B are diagrams for describing an example of biometricinformation estimation by the processor of FIG. 4.

FIG. 6 is a block diagram illustrating a processor of an apparatus formeasuring biometric information according to another exemplaryembodiment.

FIG. 7 is a block diagram illustrating a processor of an apparatus forestimation biometric information according to still another exemplaryembodiment.

FIG. 8 is a diagram for describing an example in which the processor ofFIG. 7 calibrates biometric information.

FIG. 9 is a block diagram illustrating an apparatus for estimatingbiometric information according to another exemplary embodiment.

FIG. 10 is a block diagram illustrating a configuration of a processoraccording to the exemplary embodiment of FIG. 9.

FIG. 11 is a diagram for describing an example in which the apparatus ofFIG. 9 calibrates biometric information.

FIG. 12 is a flowchart illustrating a method of estimating biometricinformation according to an exemplary embodiment.

FIG. 13 is a flowchart illustrating one embodiment of estimation ofbiometric information in the method of FIG. 12.

FIG. 14 is a flowchart illustrating a method of estimating biometricinformation according to another exemplary embodiment.

FIG. 15 is a flowchart illustrating a method of estimating biometricinformation according to still another exemplary embodiment.

DETAILED DESCRIPTION

Exemplary embodiments are described in greater detail below withreference to the accompanying drawings.

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. The matters defined inthe description, such as detailed construction and elements, areprovided to assist in a comprehensive understanding of the exemplaryembodiments. However, it is apparent that the exemplary embodiments canbe practiced without those specifically defined matters. In thefollowing description, a detailed description of known functions andconfigurations incorporated herein will be omitted when it may obscurethe subject matter with unnecessary detail.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. Also, the singular forms are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. In the specification, unless explicitly described to thecontrary, the words “comprise” and “includes” and their variations suchas “comprises,” “comprising,” “includes,” and “including,” will beunderstood to imply the inclusion of stated elements but not theexclusion of any other elements. Terms such as “ . . . unit” and“module” denote units that process at least one function or operation,and they may be implemented by using hardware, software, or acombination of hardware and software.

FIGS. 1A and 1B are diagrams illustrating the configuration of anapparatus for estimating biometric information according to an exemplaryembodiment.

The apparatus 1 for estimating biometric information may be a wearabledevice which can be worn on a user's body part. In addition, the form ofthe apparatus may not be particularly limited and may be manufactured invarious types, such as a wristwatch type, a bracelet type, a ring type,a glass-type, a hairband type, and the like. However, as shown in FIGS.1A and 1B, the apparatus 1 will be described as having a wristwatch-typefor convenience of description.

Referring to FIGS. 1A and 1B, the apparatus 1 for estimating biometricinformation includes a main body 100 and a strap 150 connected to themain body 100 and formed to be flexible to wrap around a user's wrist.

Various modules for performing various functions for estimatingbiometric information may be mounted in the main body 100. For example,the main body 100 may include a sensor module configured to measurebio-signals at a plurality of measurement sites O1 and O2 and aprocessing module configured to estimate biometric signal based on themeasured biometric signals.

From the biometrical signals, the apparatus 1 may acquire biometricinformation such as blood pressure information including a diastolicblood pressure and a systolic blood pressure. However, the biometricinformation is not limited to blood pressure information and may includea vascular age, a degree of arterial stiffness, an aortic pressurewaveform, a stress index, a degree of fatigue, and the like. Inaddition, the biometric signals measured at a plurality of sites O1 andO2 of a subject by the sensor module may include an electrocardiogram(ECG) signal, a photoplethysmography (PPG) signal (hereinafter, will bereferred to as a “pulse wave signal”), etc.

For example, at least a part of the sensor module may be disposed atposition S1 of a lower part 110 of the main body 100. Thus, when theuser wears the apparatus 1, the lower part 110 of the main body 100comes into contact with the upper part of the user's wrist (i.e., theback of the user's hand) including a first measurement site O1 so thatthe sensor module measures a biometric signal at the first measurementsite O1 (e.g., an upper part O1 of the wrist through which venous bloodor capillary blood passes). In addition, at least a part of the sensormodule may be disposed at a specific position S2 of the upper part 120of the main body 100 so as to measure a biometric signal at a secondmeasurement site O2 (e.g., a finger O2 of the hand on which theapparatus 1 is not worn).

In addition, a display module 140 may be mounted in the upper part 120of the main body 100 so as to display the biometric signal measured bythe sensor module or a processing result of the processing module. Inthis case, the display module 140 may be implemented to allow touchinput, in addition to displaying processing result, so that the displaymodule 140 can interact with the user. In addition, a communicationmodule to be connected to an external device using a wired or wirelessconnection technology to transmit and receive various data necessary forbiometric information estimation may be included. However, theembodiment is not limited to the above modules, and various modules forperforming various functions may be included.

In addition, although the sensor module is described as being mounted inthe main body 100, the position of the sensor module is not limitedthereto, and at least a part of the sensor module may be placed at thestrap 150 so as to acquire a biometric signal at a measurement site atwhich the radial artery passes. For example, the sensor module foracquiring a biometric signal at the first measurement site may bedisposed on one surface of the strap 150 which is in contact with thefirst measurement site at which the radial artery passes, and the sensormodule for acquiring a biometric signal at the second measurement sitemay be disposed on the other surface of the strap 150 which is incontact with a finger of the other hand that is the second measurementsite. Alternatively, the sensor module for acquiring a biometric signalat the first measurement site may be mounted in the strap 150, and thesensor module for acquiring a biometric signal at the second measurementsite may be mounted in the upper part 120 of the main body 100.

FIG. 2 is a block diagram illustrating an apparatus for estimatingbiometric information according to an exemplary embodiment. FIG. 3 is ablock diagram illustrating a sensor of the apparatus for estimatingbiometric information according to an exemplary embodiment. Theapparatus 200 for estimating biometric information according to thepresent exemplary embodiment may be one exemplary embodiment of theapparatus 1 illustrated in FIGS. 1A and 1B.

Referring to FIGS. 1A, 1B, and 2, the apparatus for estimating biometricinformation includes a sensor 210, a processor 220, and a display 230.

The sensor 210 may include one or more sensors to acquirer biometricsignals from a plurality of measurement sites of a user. The sensor 210may be mounted in the main body 100 or the strap 150. Hereinafter, forconvenience of description, the sensor 210 will be described as beingmounted in the main body 100. The sensor 210 may include an ECG sensor310 and a pulse wave sensor 320 to measure pulse wave signals at two ormore measurement sites.

For example, the ECG sensor 310 may include a first electrode 311 and asecond electrode 312. The first electrode 311 may be disposed at aspecific position S1 on the lower part 110 of the main body 100 suchthat the first electrode 311 is in contact with a first measurement siteO1 when the lower part 110 of the main body 100 is in close contact witha subject to be measured. In addition, the second electrode 312 may bedisposed at a specific position S2 on the upper part 120 of the mainbody 100 which is in contact with a second measurement site O2 of thesubject to be measured. In this case, the first electrode 311 may be apositive (+) electrode and the second electrode 312 may be a negative(−) electrode.

The pulse wave sensor 320 may include two or more pulse wave sensors tomeasure two or more pulse wave signals of the user. For example,referring to FIG. 3, the pulse wave sensor 320 may include a first pulsewave sensor 321 and a second pulse wave sensor 322 to measure pulse wavesignals at two sites of the subject. The first pulse wave sensor 321 maybe disposed at a specific position S1 on the lower part 110 of the mainbody 100 which is in contact with the first measurement site O1 and maymeasure a pulse wave signal from the first measurement site O1. Inaddition, the second pulse wave sensor 322 may be disposed at a specificposition S2 on the upper part 120 of the main body which is in contactwith the second measurement site O2 and may measure a pulse wave signalfrom the second measurement site O2. In this case, the first pulse wavesensor 321 and the second pulse wave sensor 322 each may include a lightsource configured to emit light and a detector configured to detectlight reflected back from the user.

When the sensor 210 receives a control signal from the processor 220 andthe second measurement site O2 of the user comes in contact with thespecific position S2 of the upper part 120 of the main body 100, the ECGsensor 310 is operated to measure an ECG and the first pulse wave sensor321 and the second pulse wave sensor 322 are operated to measure a firstpulse wave signal and a second pulse wave signal at the firstmeasurement site O1 and the second measurement site O2, respectively.

The processor 220 may be included in the main body 100 and beelectrically connected to the sensor 210. When a user's command or apredetermined condition is satisfied, the processor 220 may generate acontrol signal for controlling the sensor 210 to measure a biometricsignal. In addition, the processor 220 may receive the biometric signalsmeasured by the sensor 210, for example, the ECG, the first pulse wavesignal, and the second pulse wave signal, and estimate biometricinformation using the received biometric signals. In this case, aseparate operation unit which receives a user's command may be includedin the main body 100, and if the display module has a touch inputfunction, as described above, the user's command may be input through aninterface provided by the display module.

The display 230 may display and provide information, such as themeasured biometric signals, the estimated biometric information,biometric information-related alarming or warning information, andvarious interface screens, to the user.

FIG. 4 is a block diagram illustrating the processor 220 of theapparatus 200 for estimating biometric information according to anexemplary embodiment. FIGS. 5A and 5B are diagrams for describing anexample of biometric information estimation by the processor of FIG. 4.

One exemplary embodiment of a configuration of the processor 220 of theapparatus 200 for estimating biometric information will be describedwith reference to FIGS. 4 and 5B. As shown in FIG. 4, a processor 400includes a transit time calculator 411, a first estimator 412, a pulsewave analyzer 421, and a second estimator 422.

When an ECG signal and two or more pulse wave signals are received fromthe sensor 210, the transit time calculator 411 may calculate three ormore pulse transit time (PTT) using the received ECG and pulse wavesignals. For example, referring to FIG. 5A, an ECG signal, a first pulsewave signal (i.e., Wrist PPG), a second signal (i.e., Finger PPG) areshown. The transit time calculator 411 may calculate a firstdifferential pulse transit time DPTT1 using the ECG signal and the firstpulse wave signal, calculate a second differential pulse transit timeDPTT2 using the ECG signal and the second pulse wave signal, andcalculate a third differential pulse transit time DPTT3 using the firstpulse wave signal and the second pulse wave signal.

When the transit time calculator 411 has calculated three or moredifferential pulse transit times (DPTTs) including DPTT1, DPTT2, andDPTT3, the first estimator 412 may estimate first biometric informationBI₁ by applying the three or more DPTTs to a first estimation model F₁,as shown in Equation 1 below. In this case, the first estimation moduleF1 may be in the form of a mathematical function expression, but thefirst estimation module is not limited thereto, and may be in the formof a table in which three or more PTT-based values (e.g., PTTs intact,an average thereof, etc.) and values of biometric information to beestimated are mapped to each other. In this case, the first biometricinformation may be a blood pressure, particularly, a diastolic bloodpressure.

BI₁ =F ₁(DPTT1,DPTT2,DPTT3)  (1)

In general, when biometric information, such as a blood pressure, isestimated, blood pressure estimate error due to pre-ejection period(PEP) exists in estimated blood pressure information because a singlePTT calculated based on an ECG and one pulse wave signal is used inestimating. However, according to the present exemplary embodiment, inaddition to the ECG and one pulse wave signal, two or more differentpulse wave signals are used to calculate PTTs and the PTTs are appliedto the estimation of blood pressure, so that the blood pressure estimateerror due to the PEP can be reduced.

In addition, when the ECG and two or more pulse wave signals arereceived from the sensor 210, the pulse wave analyzer 421 may analyzedegrees of impact of reflected waves on the pulse wave signals on thebasis of the waveforms of the two or more pulse wave signals measured atmultiple sites. For example, as shown in FIGS. 5A and 5B, the pulse waveanalyzer 421 may extract feature points (e.g., inflection points of thewaveform) of the waveforms of the pulse waves PW1 and PW2 and extractreflected wave characteristic information using the extracted featurepoints in order to analyze degrees of impact of the reflected waves RW1and RW2 on the respective pulse wave signals PW1 and PW2. In this case,the pulse wave analyzer 421 may perform a second-order differentiationon each of the pulse wave signals PW1 and PW2, and extract a position ofthe pulse wave signal which corresponds to a time position correspondingto a local minimum point of the second-order differential signal as afeature point. Referring to FIG. 5B, it is seen that three featurepoints f11, f12, and f13 are extracted from the first wave signal PW1,and three feature points f21, f22, and f23 are extracted from the secondpulse wave signal PW2.

When the feature points are extracted from each pulse wave signal PW1and PW2, the pulse wave analyzer 421 may extract a first PTT or a secondPTT as the reflected wave characteristic information using the featurepoints of the different pulse wave signals or the feature points of thesame pulse wave signal. For example, as shown in FIG. 5B, the pulse waveanalyzer 421 may extract three first PTTs SPTT1, SPTT2, and SPTT3 usingtime differences between the corresponding feature points f11-f21,f12-22, and f13-f23 between the first pulse wave signal PW1 and thesecond pulse wave signal PW2, and extract second PTTs PPTT1 and PPTT2using a time difference between the feature points f11-f13 in the firstpulse wave signal PW1 and a time difference between the feature pointsf21-f23 in the second pulse wave signal PW2.

When the reflected wave characteristic information is extracted, thesecond estimator 422 may extract second biometric information byapplying the extracted reflected wave characteristic information to asecond estimation model. In addition, the second estimation model may bein the form of a mathematical function expression like the firstestimation model, but is not limited thereto. In this case, the secondestimator 422 may estimate the second biometric information BI₂, asshown in the following Equation 2, by adding the first biometricinformation BI₁, which is estimated by the first estimator 412, to acalculation result obtained by applying the reflected wavecharacteristic information SPTT1, SPTT2, SPTT3, PPTT1, and PPTT2 to thesecond estimation model. In this case, the second biometric informationmay be blood pressure information, particularly, systolic blood pressureinformation.

BI₂=BI₁ +F ₂(SPTT1,SPTT2,SPTT3,PPTT1,PPTT2)   (2)

In general, in the case of a systolic blood pressure, a waveform of thepulse wave signal is considerably affected by a reflected wave.Therefore, according to the present exemplary embodiment, the systolicblood pressure is measured, separately from the measurement of adiastolic blood pressure, by applying the degree of impact of thereflected wave and thereby it is possible to reduce the estimationerror.

FIG. 6 is a block diagram illustrating a processor of an apparatus formeasuring biometric information according to another exemplaryembodiment.

Referring to FIG. 6, the processor 600 may include a transit timecalculator 411, a first estimator 412, a pulse wave analyzer 421, asecond estimator 422, and an estimation model generator 610. The transittime calculator 411, the first estimator 412, the pulse wave analyzer421, and the second estimator 422 are described above and thus detaileddescriptions thereof will be omitted.

The estimator model generator 610 may generate or update an estimationmodel necessary for estimating biometric information, that is, the firstestimation model and the second estimation model, which are describedabove, when a user's request or a predetermined condition is satisfied.The estimation model generator 610 may generate an estimation model atthe time when the user registers to use the apparatus 200 for estimationbiometric information for the first time. In addition, the estimationmodel generator 610 may generate or update the estimation model at thetime point requested by the user or at a predetermined interval.

The estimation model generator 610 may receive personal information,such as age, sex, height, weight, health condition, and the like, fromthe user so that the user's personal characteristics can be applied tothe estimation model. The user's personal information may be used as afactor to limit a value of biometric information to be estimated in theestimation model. For example, in estimating a blood pressure, suchpersonal information may be used to limit the range of the maximum valueand the minimum value of a blood pressure.

The estimation model generator 610 may control the sensor 210 to measurebiometric signals for a predetermined time (e.g., 4 hours) at apredetermined interval (e.g., 15 minutes) in response to an estimationmodel generation request, and collect measured biometric signals aslearning data. The estimation model generator 610 may also collect, aslearning data, a value of actual biometric information estimated by anexternal apparatus for estimating biometric information, for example, ablood pressure measured by a cuff-type blood pressure measuring device.However, the exemplary embodiment is not limited to the above examples,and the user's various information of the user, such as a peripheralvascular resistance value, blood viscosity, a stroke volume, and thelike, may be collected as learning data.

The estimation model generator 610 may generate or update the estimationmodel using the collected personal information and learning data.

FIG. 7 is a block diagram illustrating a processor of an apparatus forestimation biometric information according to still another exemplaryembodiment. FIG. 8 is a diagram for describing an example in which theprocessor of FIG. 7 calibrates biometric information.

Referring to FIG. 7, the processor 700 according to the presentexemplary embodiment includes a transit time calculator 411, a firstestimator 412, a pulse wave analyzer 421, a second estimator 422, and acalibrator 710. The transit time calculator 411, the first estimator412, the pulse wave analyzer 421, and the second estimator 422 aredescribed above, and hence detailed descriptions thereof will beomitted.

The calibrator 710 may estimate additional information using two or morepulse wave signals measured by the sensor 210, and may allow the firstestimator 412 and the second estimator 422 to further estimate firstbiometric information and second biometric information, respectively, bytaking into consideration the estimated additional information.Alternatively, when a model that represents a correlation between thefirst and second biometric information and the additional information isestablished in advance, the calibrator 710 may directly calibrate thefirst biometric information and the second biometric information usingthe additional information. In this case, the additional informationincludes information, such as a peripheral vascular resistance value,but is not limited thereto.

For example, the calibrator 710 may estimate the pattern of blood vesselwave propagation using the waveforms of a first pulse wave signal and asecond pulse wave signal measured by the sensor 210, and may obtaininformation, such as a peripheral vascular resistance value, by applyingthe estimated propagation pattern to a vascular resistance estimationmodel. In this case, the vascular resistance estimation model may begenerated for which a vascular resistance estimation model representingthe pattern of the blood vessel wave propagation from the aorta to theradial artery and the carotid artery is fitted to the propagationpatterns of the first pulse wave signal measured at a wrist and thesecond pulse wave signal measured at a finger.

With reference to FIG. 8, waveform R1 indicates a waveform when that isgenerated when the resistance of a peripheral blood vessel of a hand is0, and waveform R2 shows a degree of deformation of the waveform R1 thatis deformed due to the resistance of the peripheral blood vessel. Thewaveforms R1 and R2 may be applied to the vascular resistance estimationmodel to estimate the peripheral vascular resistance value. For example,the calibrator 710 may assume that the waveform of the first pulse wavesignal measured at the wrist is a waveform when there is no resistance(e.g., waveform R1) and the waveform of the second pulse wave signalmeasured at the finger is a waveform when there is a resistance (e.g.,waveform R2), and apply a difference between the two waveforms (e.g.,waveforms R1 and R2) to the vascular resistance estimation model toestimate the peripheral vascular resistance information.

In this case, the first estimator 412 may estimate the first biometricinformation by applying the PTTs PTT1, PTT2, and PTT3 measured by thetransit time calculator 411 and peripheral vascular resistanceinformation TPR to a first estimation model F₁, as shown in Equation 3.

BI₁ =F ₁(DPTT1,DPTT2,DPTT3,TPR)  (3)

In the same manner, the second estimator 422 may estimate the secondbiometric information by applying the reflected wave characteristicinformation PPTT1, PPTT2, SPTT1, SPTT2, and SPTT3 measured by the pulsewave analyzer 421 and the peripheral vascular resistance information TPRto a second estimation model F₂, as shown in Equation 4.

BI₂ =F ₂(SPTT1,SPTT2,SPTT3,PPTT1,PPTT2,TPR)  (4)

FIG. 9 is a block diagram illustrating an apparatus for estimatingbiometric information according to another exemplary embodiment. FIG. 10is a block diagram illustrating a configuration of a processor accordingto the embodiment of FIG. 9. FIG. 11 is a diagram for describing anexample in which the apparatus of FIG. 9 calibrates biometricinformation. The apparatus 900 for estimating biometric informationaccording to the present embodiment may be another exemplary embodimentof the apparatus 1 shown in FIGS. 1A and 1B.

Referring to FIG. 9, the apparatus 900 for estimating biometricinformation includes a sensor 910, a processor 920, and a communicator930. The communicator 903 may be implemented with a communicationinterface. The apparatus 900 according to the present exemplaryembodiment may receive reference biometric information from an externalapparatus for estimating biometric information and calibrate currentbiometric information using the received reference biometricinformation. The sensor 910 may include an ECG sensor and two or morepulse wave sensors, as described with reference to FIG. 3, and maymeasure an ECG and a pulse wave signal at two or more measurement sites.The processor 920 includes a transit time calculator 1011, a firstestimator 1012, a pulse wave analyzer 1021, a second estimator 1022, anestimation model 1030, and a calibrator 1040, and estimates biometricinformation using biometric signals measured by the sensor 910. Thesensor 910 and the processor 920 have been described in detail above andthus the following description will focus on functions that are notstated.

The processor 920 may communicate with external apparatus 950 forestimating biometric information by controlling the communicator 930 inresponse to a user's calibration command. In this case, thecommunication technology may include, but is not limited to, a Bluetoothcommunication, Bluetooth low energy (BLE) communication, a near-fieldcommunication (NFC), a wireless local area network (WLAN) communication,a ZigBee communication, an infrared data association (IrDA)communication, a Wi-Fi direct (WFD) communication, a ultra-wideband(UWB) communication, an Ant+ communication, a Wi-Fi communication, and amobile communication.

When the communication connection to the external apparatus 950 forestimating biometric information is successful, the processor 920controls the sensor 910 to measure a biometric signal of the user whilethe external apparatus 950 estimates the biometric information of theuser. However, the exemplary embodiment is not limited to the case wherethe external apparatus 950 is operated at the same time.

When the biometric signal is estimated by the sensor 910, the transittime calculator 1011 and the first estimator 1012 may estimate firstbiometric information and the pulse wave analyzer 1021 and the secondestimator 1022 may estimate second biometric information.

When the external apparatus 950 for estimating biometric informationcompletes estimating the biometric information, the communicator 930 mayreceive the estimated biometric information as reference biometricinformation and forward the biometric information to the calibrator1040.

The calibrator 1040 may calibrate the values of biometric informationestimated by the first estimator 1012 and the second estimator 1022using the received reference biometric information. Alternatively, thecalibrator 1040 may calibrate the estimation model 1030 required toestimate the biometric information. In this case, the estimation model1030 may be stored in a storage module as a first estimation model and asecond estimation model. The storage module may include at least onetype of memory, such as a flash memory, a hard disk, a micro typemultimedia card, and a card type memory (e.g., SD or XD memory), arandom access memory (RAM), a static random access memory (SRAM), a readonly memory (ROM), an electrically erasable programmable read onlymemory (EEPROM), a programmable read only memory (PROM), a magneticmemory, a magnetic disk, and an optical disk, but is not limitedthereto.

Alternatively, the calibrator 1040 may calibrate the PTTs calculated bythe transit time calculator 1011 and the reflected wave characteristicinformation acquired by the pulse wave analyzer 1021. In this case, theexternal apparatus 950 may be a cuff-type blood pressure measuringdevice, and the reference biometric information may include measuredblood pressure information and cuff pressure information measured atmultiple sites.

For example, referring to FIG. 11, when the user inputs a calibrationcommand while wearing the wristwatch-type cuffless blood pressureestimating apparatus 1110, the cuffless blood pressure estimatingapparatus 1110 may be connected with a cuff-type blood pressuremeasuring apparatus 1120 to receive blood pressure information or cuffpressure information, and calibrate information related to the bloodpressure information estimated by the cuffless blood pressure estimatingapparatus 110, using the received blood pressure information or cuffpressure information.

FIG. 12 is a flowchart illustrating a method of estimating biometricinformation according to an exemplary embodiment. FIG. 13 is a flowchartillustrating one embodiment of estimation of biometric information inthe method of FIG. 12. The method of FIG. 12 may be one embodiment of abiometric information estimation method performed by the apparatus 200of FIG. 2. Since the operations have been described in detail above, abrief description will be made in order to minimize redundantdescription.

First, the apparatus 200 for estimating biometric information obtains anECG signal and two or more pulse wave signals from a user in operation1210. According to one embodiment, the apparatus 200 may include an ECGsensor and two or more pulse wave sensors to measure biometric signalsat a plurality of sites so that biometric information can be estimatedusing a plurality of biometric signals, for example, an ECG signal andpulse wave signals.

Then, when the ECG and the two or more pulse wave signals are measured,the biometric information is estimated using the ECG signal and two ormore pulse wave signals measured in operation 1220. The biometricinformation may be displayed to the user in operation 1230.

One exemplary embodiment of operation 1220 of the biometric informationestimation will be described in detail with reference to FIG. 13. First,three or more PTTs are calculated using the ECG signal and two or morepulse wave signals measured in operation 1311.

Then, first biometric information is estimated by applying thecalculated three or more PTTs to a first estimation model, in operation1312. According to the present exemplary embodiment, the first biometricinformation, for example, a diastolic blood pressure, is estimated bytaking into consideration the PTTs which have been calculated usingdifferent pulse wave signals, and thereby it is possible to reduce bloodpressure estimation error due to PEP.

In addition, when the ECG and two or more pulse wave signals areobtained in operation 1210, reflected wave characteristic information isextracted using the two or more pulse wave signals in operation 1321.For example, feature points may be extracted from a first pulse wavesignal and a second pulse wave signal, a PTT may be calculated usingfeature points of both the first and second pulse wave signals, a PTTmay be calculated using the feature points in the first pulse wavesignal and the feature points in the second pulse wave signal, and thecalculated PTTs may be extracted as the reflected wave characteristicinformation.

Thereafter, second biometric information is estimated by applying theextracted reflected wave characteristic information to a secondestimation model in operation 1322. In this case, the second biometricinformation may be a systolic blood pressure. The systolic bloodpressure may be calculated as a value of the second biometricinformation by adding the value of first biometric information estimatedin operation 1312 to a value obtained by applying the extractedreflected wave characteristic information to the second estimationmodel.

Then, it is determined whether it is needed to calibrate the biometricinformation using additional information in operation 1330. In thiscase, whether or not the biometric information calibration is necessarymay be set in advance based on various types of information, such as arequired accuracy of the biometric information estimation, a batterystatus of the apparatus, a type of biometric information, and the like.For example, in a case where it is necessary to estimate biometricinformation more accurately even if a relatively long time is requiredto estimate the biometric information, for example, in the case of apatient having a disease, such as hypertension or hypotension,calibration in consideration of additional information, such asperipheral vascular resistance information, may be necessary.

Then, when it is determined that the calibration is necessary,peripheral vascular resistance information is extracted by analyzingwaveforms of the two or more pulse wave signals in operation 1340. Then,the first biometric information and the second biometric information arecalibrated based on the extracted peripheral vascular resistanceinformation in operation 1350.

However, FIG. 13 illustrates that operations 1330, 1340 and 1350 areperformed after operations 1312 and 1322 are completed, but theexemplary embodiment is not limited thereto. Operations 1330 and 1340may be performed before operations 1312 and 1322. In this case, thefirst biometric information may be estimated based on the extractedperipheral vascular resistance information together with the PTTs inoperation 1312 and the second biometric information may be estimatedbased on the reflected wave characteristic information in operation1322.

FIG. 14 is a flowchart illustrating a method of estimating biometricinformation according to another exemplary embodiment.

The method of FIG. 14 may be one exemplary embodiment of a biometricinformation estimation method performed by the apparatus 200 to whichthe exemplary embodiment of the processor 600 of FIG. 6 is applied. FIG.14 separately shows a process of generating or updating an estimationmodel for estimating biometric information, but the process may beperformed in parallel with or before the operations described withreference to FIGS. 12 and 13.

The apparatus 200 for estimating biometric information receives arequest signal for generating or updating an estimation model from auser or determines whether to generate or update the estimation model bychecking specific conditions set in advance, in operation 1410.

Then, personal information for applying personal characteristics isreceived from the user in operation 1420. In this case, the personalinformation may be applied to an estimation model so that biometricinformation can be more accurately estimated in consideration of thehealth condition and age of the user.

Then, biometric signals, such as an ECG and pulse wave signals, arecollected as learning data by controlling a sensor for a predeterminedtime period in operation 1430. In this case, cuff blood pressureinformation measured by a cuff-type blood pressure measuring device maybe additionally collected as learning data. However, the presentexemplary embodiment is not limited to the above-described information,and various types of additional information, such as peripheral vascularresistance information, blood viscosity, a stroke volume, and the like,may be collected as learning data.

Thereafter, an estimation model is generated or updated using thepersonal information and the learning data in operation 1440.

FIG. 15 is a flowchart illustrating a method of estimating biometricinformation according to still another exemplary embodiment.

The method of FIG. 15 is one exemplary embodiment of a biometricinformation calibration method performed by the apparatus 900 forestimating biometric information shown in FIG. 9.

First, the apparatus 900 receives, from a user, a request forcalibration using an external apparatus for estimating biometricinformation in operation 1510.

Then, the apparatus 900 is connected to the external apparatus via wiredor wireless communication in operation 1520, and receives referencebiometric information measured when the external apparatus completes themeasurement in operation 1530. In this case, the external apparatus maybe a cuff-type blood pressure measuring apparatus, and the referencebiometric information may include a blood pressure and cuff pressureinformation.

Also, when the communication connection with the external apparatus iscompleted, the apparatus 900 obtains an ECG signal and pulse wavesignals by controlling a sensor in operation 1540. At this time, thesensor may obtain two or more pulse wave signals from two measurementsites, e.g., a wrist and a finger.

Then, biometric information is estimated on the basis of the ECG signaland two or more pulse wave signals measured in operation 1560. Asdescribed above, first biometric information (e.g., a diastolic bloodpressure) may be estimated by calculating PTTs using the ECG signal andtwo or more pulse wave signals, and second biometric information (e.g.,a systolic blood pressure) may be estimated using reflected wavecharacteristics of the two or more pulse wave signals.

Then, the first biometric information and the second biometricinformation, which are estimated on the basis of the reference biometricinformation received from the external apparatus, are calibrated inoperation 1560. In FIG. 5, operation 1560 is described as beingperformed after operation 1550, but this is merely an example. Anestimation model may be calibrated using the reference biometricinformation before operation 1550 or the biometric information may beestimated in operation 1550 after the PTTs or the reflected wavecharacteristic information is calibrated.

While not restricted thereto, an exemplary embodiment can be implementedas computer readable codes in a computer readable record medium. Thecomputer-readable recording medium is any data storage device that canstore data that can be thereafter read by a computer system. Thecomputer readable record medium includes all types of record media inwhich computer readable data are stored. Examples of the computerreadable record medium include a read-only memory (ROM), a random-accessmemory (RAM), a compact disk ROM (CD-ROM), a magnetic tape, a floppydisk, and an optical data storage. Further, the record medium may beimplemented in the form of a carrier wave such as Internet transmission.In addition, the computer readable record medium may be distributed tocomputer systems over a network, in which computer readable codes may bestored and executed in a distributed manner. Also, an exemplaryembodiment may be written as a computer program transmitted over acomputer-readable transmission medium, such as a carrier wave, andreceived and implemented in general-use or special-purpose digitalcomputers that execute the programs. Moreover, it is understood that inexemplary embodiments, one or more units of the above-describedapparatuses and devices can include circuitry, a processor, amicroprocessor, etc., and may execute a computer program stored in acomputer-readable medium.

The foregoing exemplary embodiments are merely exemplary and are not tobe construed as limiting. The present teaching can be readily applied toother types of apparatuses. Also, the description of the exemplaryembodiments is intended to be illustrative, and not to limit the scopeof the claims, and many alternatives, modifications, and variations willbe apparent to those skilled in the art.

What is claimed is:
 1. A wristwatch-type device comprising: a main body; a strap connected to the main body and configured to wrap around a wrist of a user; a display provided on the main body and configured to receive a touch input from the user; an electrocardiogram (ECG) sensor comprising: a first electrode disposed at a lower part of the main body to be in contact with the wrist of the user when the user wears the wristwatch-type device; and a second electrode disposed at a first part of the main body, which is different from the lower part, to be in contact with a finger of the user, wherein the ECG sensor is configured to measure an ECG signal of the user using the first electrode and the second electrode, a first pulse wave sensor disposed at the lower part of the main body and configured to measure a wrist photoplethysmogram (PPG) signal at the wrist; a second pulse wave sensor disposed at a second part of the main body, which is different from the lower part, and configured to measure a finger PPG signal from the user; and a processor configured to, based on the touch input, obtain biometric information using the ECG signal, the finger PPG signal, and the wrist PPG signal.
 2. The wristwatch-type device of claim 1, wherein the processor is further configured to, based on a command that is input through an interface provided by the display, control the ECG sensor, the first pulse wave sensor, and the second pulse wave sensor to measure the ECG signal, the wrist PPG signal and the finger PPG signal, respectively.
 3. The wristwatch-type device of claim 1, wherein the processor is further configured to determine a plurality of pulse transit times using the ECG signal, the wrist PPG signal, and the finger PPG signal and obtain the biometric information based on the plurality of pulse transit times.
 4. The wristwatch-type device of claim 1, wherein the ECG sensor, the first pulse wave sensor, and the second pulse wave sensor are configured to simultaneously measure the ECG signal, the finger PPG signal, and the wrist PPG signal, respectively.
 5. The wristwatch-type device of claim 1, wherein the display is further configured to display the biometric information.
 6. The wristwatch-type device of claim 1, wherein the display is further configured to display at least one of the ECG signal, the wrist PPG signal and the finger PPG signal.
 7. The wristwatch-type device of claim 1, wherein the first electrode and the second electrode correspond to a positive electrode and a negative electrode of the ECG sensor, respectively.
 8. The wristwatch-type device of claim 1, wherein the first electrode and the second electrode correspond to a negative electrode and a positive electrode of the ECG sensor, respectively.
 9. The wristwatch-type device of claim 1, wherein the biometric information comprises a blood pressure level of the user.
 10. The wristwatch-type device of claim 1, wherein the biometric information comprises a stress level of the user.
 11. The wristwatch-type device of claim 1, wherein the processor is further configured to calibrate the biometric information based on reference biometric information.
 12. The wristwatch-type device of claim 1, wherein the first part is different from the second part.
 13. A wrist-type wearable device comprising: a main body; a strap connected to the main body and configured to wrap around a wrist of a user; a display provided on the main body; an electrocardiogram (ECG) sensor comprising: a first electrode disposed at a first part of the main body; and a second electrode disposed at a second part of the main body, which is different from the first part, wherein the ECG sensor is configured to measure an ECG signal of the user based on a contact between the first electrode and the wrist of the user and a contact between the second electrode and a finger of the user, a first pulse wave sensor configured to measure a wrist photoplethysmogram (PPG) signal based on a contact with the wrist of the user; a second pulse wave sensor disposed at a third part of the main body, which is different from the first part, and configured to measure a finger PPG signal from the user; and a processor configured to obtain biometric information using the ECG signal, the finger PPG signal, and the wrist PPG signal.
 14. The wrist-type wearable device of claim 13, wherein the first electrode is disposed at a specific position on the first part of the main body such that the first electrode is in contact with the wrist when the wrist-type wearable device is worn on the user, and wherein the first pulse wave sensor is disposed at the specific position on the first part of the main body.
 15. The wrist-type wearable device of claim 13, wherein the display is further configured to provide an interface via which a touch input of a command is received, and wherein the processor is further configured to control the ECG sensor, the first pulse wave sensor, and the second pulse wave sensor to measure the ECG signal, the wrist PPG signal and the finger PPG signal, respectively, based on the touch input.
 16. The wrist-type wearable device of claim 13, wherein the processor is further configured to determine a plurality of pulse transit times using the ECG signal, the wrist PPG signal, and the finger PPG signal and obtain the biometric information based on the plurality of pulse transit times.
 17. The wrist-type wearable device of claim 16, wherein the processor is further configured to determine the plurality of pulse transit times using the ECG signal, the wrist PPG signal, and the finger PPG signal that are simultaneously measured.
 18. The wrist-type wearable device of claim 13, wherein the second part is different from the third part. 